摘要
Machine learning is currently the most active interdisciplinary field having numerous applications; additionally, machine-learning techniques are used to research quantum many-body problems. In this study, we first propose neural network quantum states(NNQSs) with general input observables and explore a few related properties, such as the tensor product and local unitary operation. Second, we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS. Finally, to quantify the approximation degree of a given pure state, we define the best approximation degree using normalized NNQSs. Furthermore, we observe that some N-qubit states can be represented by a normalized NNQS, such as separable pure states, Bell states and GHZ states.
Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.
基金
supported by the National Natural Science Foundation of China(Grant Nos.11871318,11771009,11571213,and 11601300)
the Fundamental Research Funds for the Central Universities(Grant Nos.GK201703093,and GK201801011)
the Natural Science Basic Research Plan in Shaanxi Province of China(Grant No.2018JM1020)
the Shaanxi Province Innovation Ability Support Program(Grant No.2018KJXX-054)
the Subject Research Project of Yuncheng University(Grant No.XK-2018032)
